#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally) ; library(ggpubr)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr) ; library(kableExtra)
library(biomaRt)
library(clusterProfiler) ; library(ReactomePA) ; library(DOSE) ; library(org.Hs.eg.db)
library(WGCNA)
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}
Load preprocessed dataset (preprocessing code in 01_data_preprocessing.Rmd) and clustering (pipeline in 05_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_01-03-2020_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'Others', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='Others' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org') ## Gencode v19
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=genes_info$ID, mart=mart)
genes_info = genes_info %>% left_join(gene_names, by=c('ID'='ensembl_gene_id'))
clustering_selected = 'DynamicHybrid'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
load('./../Data/GSEA.RData')
GSEA_SFARI = enrichment_SFARI
load('./../Data/ORA.RData')
ORA_SFARI = enrichment_SFARI
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds, enrichment_DGN, enrichment_DO, enrichment_GO,
enrichment_KEGG, enrichment_Reactome, enrichment_SFARI)
We have results both from GSEA and ORA to measure the enrichment of SFARI Genes in each module, and they both agree with each other relatively well
SFARI_genes_by_module = c()
for(module in names(GSEA_SFARI)){
GSEA_info = GSEA_SFARI[[module]] %>% dplyr::select(ID, pvalue, p.adjust, NES) %>%
mutate(pvalue = ifelse(NES>0, pvalue, 1-pvalue),
p.adjust = ifelse(NES>0, p.adjust, 1)) %>%
dplyr::rename('GSEA_pval' = pvalue, 'GSEA_padj'= p.adjust)
ORA_info = ORA_SFARI[[module]] %>% dplyr::select(ID, pvalue, p.adjust, qvalue, GeneRatio, Count) %>%
dplyr::rename('ORA_pval' = pvalue, 'ORA_padj' = p.adjust)
module_info = GSEA_info %>% full_join(ORA_info, by = 'ID') %>% add_column(.before = 'ID', Module = module)
SFARI_genes_by_module = rbind(SFARI_genes_by_module, module_info)
}
SFARI_genes_by_module = SFARI_genes_by_module %>%
left_join(dataset %>% dplyr::select(Module, MTcor) %>%
group_by(Module,MTcor) %>% tally %>% ungroup, by = 'Module') %>%
mutate(ORA_pval = ifelse(is.na(ORA_pval), 1, ORA_pval),
ORA_padj = ifelse(is.na(ORA_padj), 1, ORA_padj))
plot_data = SFARI_genes_by_module %>% filter(ID=='SFARI')
ggplotly(plot_data %>% ggplot(aes(1-GSEA_pval, 1-ORA_pval, size = n)) +
geom_point(color = plot_data$Module, alpha = .7, aes(id=Module)) +
geom_smooth(se=FALSE, color = '#CCCCCC') +
xlab('GSEA Enrichment') + ylab('ORA Enrichment') + coord_fixed() +
ggtitle(paste0('Corr = ', round(cor(plot_data$GSEA_pval, plot_data$ORA_pval),2))) +
theme_minimal() + theme(legend.position = 'none'))
To determine which modules have a statistically significant enrichment in SFARI Genes we can use the adjusted p-values. We used the Bonferroni correction for this.
GSEA identifies 15/91 as significant. This doesn’t make sense
ggplotly(plot_data %>% ggplot(aes(GSEA_padj, ORA_padj, size = n)) +
geom_point(color = plot_data$Module, alpha = .7, aes(id=Module)) +
geom_smooth(se=FALSE, color = '#CCCCCC') +
geom_hline(yintercept = 0.01, color = 'gray', linetype = 'dashed') +
geom_vline(xintercept = 0.01, color = 'gray', linetype = 'dashed') +
xlab('GSEA adjusted p-value') + ylab('ORA adjusted p-value') +
scale_x_log10(limits = c(min(plot_data$GSEA_padj, plot_data$ORA_padj),1.2)) +
scale_y_log10(limits = c(min(plot_data$GSEA_padj, plot_data$ORA_padj),1.2)) +
ggtitle(paste0('Corr = ',round(cor(plot_data$GSEA_padj, plot_data$ORA_padj),2))) + coord_fixed() +
theme_minimal() + theme(legend.position = 'none'))
plot_data = plot_data %>% mutate(GSEA_sig = GSEA_padj<0.01, ORA_sig = ORA_padj<0.01) %>%
apply_labels(GSEA_sig = 'GSEA significant enrichment',
ORA_sig = 'ORA significant enrichment')
cro(plot_data$GSEA_sig, list(plot_data$ORA_sig, total()))
|  ORA significant enrichment |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  GSEA significant enrichment | ||||
| Â Â Â FALSEÂ | 75 | 1 | Â | 76 |
| Â Â Â TRUEÂ | 13 | 2 | Â | 15 |
|    #Total cases | 88 | 3 |  | 91 |
The ‘over-enrichment’ in SFARI Modules in GSEA could be because SFARI Genes have in general higher Module Memberships than the other genes, which would make them cluster at the beginning of the list constantly and would bias the enrichment analysis.
Looking at the plot below, we can see that there is not a uniform distribution of SFARI genes across all quantiles of the Module Membership values, but they instead seem to cluster around Module Membership values with high magnitudes (both positive and negative), so I don’t think the GSEA results for the SFARI genes are valid.
Because of this, I’m going to use the enrichment from the ORA to study the SFARI Genes
quant_data = dataset %>% dplyr::select(ID, contains('MM.')) %>%
left_join(genes_info %>% dplyr::select(ID, gene.score), by = 'ID') %>% dplyr::select(-ID) %>%
melt %>% mutate(quant = cut(value, breaks = quantile(value, probs = seq(0,1,0.05)) %>%
as.vector, labels = FALSE)) %>%
group_by(gene.score, quant) %>% tally %>% ungroup %>% ungroup
quant_data = quant_data %>% group_by(quant) %>% summarise(N = sum(n)) %>% ungroup %>%
left_join(quant_data, by = 'quant') %>% dplyr::select(quant, gene.score, n, N) %>%
mutate(p = round(100*n/N,2)) %>% filter(!is.na(quant)) %>%
mutate(gene.score = factor(gene.score, levels = rev(c('1','2','3','Neuronal','Others'))))
ggplotly(quant_data %>% filter(!gene.score %in% c('Neuronal','Others')) %>%
ggplot(aes(quant, p, fill = gene.score)) + geom_bar(stat='identity') +
xlab('Module Membership Quantiles') + ylab('% of SFARI Genes in Quantile') +
ggtitle('Percentage of Genes labelled as SFARI in each Quantile') +
scale_fill_manual(values = SFARI_colour_hue(r=rev(c(1:3)))) +
theme_minimal() + theme(legend.position = 'none'))
rm(quant_data)
Selecting modules with an adjusted p-value below 0.01 using the ORA
ggplotly(plot_data %>% ggplot(aes(MTcor, ORA_padj, size=n)) +
geom_point(color=plot_data$Module, alpha=0.5, aes(id=Module)) +
geom_hline(yintercept = 0.01, color = 'gray', linetype = 'dotted') +
xlab('Module-Diagnosis Correlation') + ylab('Corrected p-values') + scale_y_log10() +
ggtitle('Modules Significantly Enriched in SFARI Genes') +
theme_minimal() + theme(legend.position = 'none'))
top_modules = plot_data %>% arrange(desc(ORA_padj)) %>% filter(ORA_padj<0.01) %>% pull(Module) %>% as.character
plot_data %>% filter(Module %in% top_modules) %>% arrange(ORA_pval) %>%
dplyr::select(Module, MTcor, ORA_pval, ORA_padj, qvalue, GeneRatio, Count) %>%
rename( ORA_pval = 'p-value', ORA_padj = 'Adjusted p-value') %>%
kable %>% kable_styling(full_width = F)
| Module | MTcor | p-value | Adjusted p-value | qvalue | GeneRatio | Count |
|---|---|---|---|---|---|---|
| #FD61D0 | 0.1879079 | 0.0000000 | 0.0000002 | 0.0000001 | 16/56 | 16 |
| #00B933 | 0.3460181 | 0.0001397 | 0.0006985 | 0.0002206 | 54/575 | 54 |
| #00BBDA | -0.4208181 | 0.0007684 | 0.0038420 | 0.0016177 | 27/247 | 27 |
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>%
left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, PC1, PC2, Module, gene.score) %>%
mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules),
alpha = ifelse(ImportantModules=='Others', 0.2, 0.4),
gene_id = paste0(ID, ' (', external_gene_id, ')')) %>%
apply_labels(ImportantModules = 'Top Modules')
plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) +
geom_point(alpha=plot_data$alpha, color=plot_data$color, aes(ID=gene_id)) + theme_minimal() +
xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1],2),'%)')) +
ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2],2),'%)')) +
ggtitle('Genes belonging to the Modules with the highest SFARI Genes Enrichment')
rm(pca)
Following the WGCNA pipeline, selecting the genes with the highest Module Membership and Gene Significance
Ordered by \(\frac{MM+|GS|}{2}\)
There aren’t that many SFARI genes in the top genes of the modules
create_table = function(module){
top_genes = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, paste0('MM.',gsub('#','',module)), GS, gene.score) %>%
filter(dataset$Module==module) %>% dplyr::rename('MM' = paste0('MM.',gsub('#','',module))) %>%
mutate(Relevance = (MM+abs(GS))/2) %>% arrange(by=-Relevance) %>% top_n(20) %>%
dplyr::rename('Gene Symbol' = external_gene_id, 'SFARI Score' = gene.score)
return(top_genes)
}
top_genes = list()
for(i in 1:length(top_modules)) top_genes[[i]] = create_table(top_modules[i])
kable(top_genes[[1]] %>% dplyr::select(-ID), caption=paste0('Top 20 genes for Module ', top_modules[1],
' (MTcor = ', round(dataset$MTcor[dataset$Module == top_modules[1]][1],2),')')) %>%
kable_styling(full_width = F)
| Gene Symbol | MM | GS | SFARI Score | Relevance |
|---|---|---|---|---|
| RHOBTB2 | 0.5492828 | -0.6440469 | Others | 0.5966649 |
| GPR27 | 0.7092604 | -0.4702614 | Others | 0.5897609 |
| CYTH3 | 0.7638147 | -0.3977080 | Others | 0.5807613 |
| TTYH3 | 0.6707149 | -0.4758652 | Others | 0.5732900 |
| SLC6A1 | 0.6913062 | -0.4290180 | 1 | 0.5601621 |
| OAT | 0.5774140 | -0.5207602 | Others | 0.5490871 |
| ARNT2 | 0.5590371 | -0.5339837 | 3 | 0.5465104 |
| YWHAH | 0.6923178 | -0.3935747 | Others | 0.5429462 |
| SERINC1 | 0.6190739 | -0.4521134 | Others | 0.5355937 |
| ACTR3B | 0.6413435 | -0.4275961 | Others | 0.5344698 |
| MARCH4 | 0.4800645 | -0.5548974 | Others | 0.5174810 |
| DNAJC6 | 0.6409081 | -0.3927409 | Others | 0.5168245 |
| SLC25A4 | 0.5233769 | -0.4903237 | Others | 0.5068503 |
| WSB2 | 0.6454349 | -0.3416964 | Others | 0.4935656 |
| PNMA1 | 0.7262341 | -0.2592167 | Others | 0.4927254 |
| TELO2 | 0.4377163 | -0.5445601 | Others | 0.4911382 |
| GNB1 | 0.6428579 | -0.3347353 | Others | 0.4887966 |
| SPRN | 0.5276961 | -0.4423674 | Others | 0.4850318 |
| ILF3 | 0.6767221 | -0.2920871 | Others | 0.4844046 |
| DDX3X | 0.5132590 | -0.4539177 | 1 | 0.4835884 |
kable(top_genes[[2]] %>% dplyr::select(-ID), caption=paste0('Top 20 genes for Module ', top_modules[2],
' (MTcor = ', round(dataset$MTcor[dataset$Module == top_modules[2]][1],2),')')) %>%
kable_styling(full_width = F)
| Gene Symbol | MM | GS | SFARI Score | Relevance |
|---|---|---|---|---|
| SLC4A7 | 0.8026955 | 0.6737220 | Others | 0.7382087 |
| PAK3 | 0.8356130 | 0.5818014 | Others | 0.7087072 |
| TCF4 | 0.7640326 | 0.5478270 | 1 | 0.6559298 |
| CCDC88A | 0.8409032 | 0.4023141 | Others | 0.6216086 |
| RSF1 | 0.9344956 | 0.2827544 | Others | 0.6086250 |
| ZBTB20 | 0.6820465 | 0.4960797 | 1 | 0.5890631 |
| SMC3 | 0.8615803 | 0.3105801 | 3 | 0.5860802 |
| PCDH7 | 0.7962620 | 0.3753828 | Others | 0.5858224 |
| UBXN4 | 0.7543204 | 0.4164262 | Others | 0.5853733 |
| ITSN2 | 0.7955002 | 0.3693109 | Others | 0.5824055 |
| KMT2E | 0.8167611 | 0.3444643 | 2 | 0.5806127 |
| PPP1R12A | 0.8137440 | 0.3437888 | Others | 0.5787664 |
| MTDH | 0.6799727 | 0.4768581 | Others | 0.5784154 |
| ZCRB1 | 0.6517623 | 0.5046602 | Others | 0.5782113 |
| MECP2 | 0.6372228 | 0.5160819 | 1 | 0.5766523 |
| ATRX | 0.8122093 | 0.3208853 | 1 | 0.5665473 |
| DDX21 | 0.6730998 | 0.4597490 | Others | 0.5664244 |
| SRSF11 | 0.7288430 | 0.4026356 | 2 | 0.5657393 |
| SREK1 | 0.7476646 | 0.3806850 | Others | 0.5641748 |
| KDM5A | 0.8216399 | 0.3055697 | Others | 0.5636048 |
kable(top_genes[[3]] %>% dplyr::select(-ID), caption=paste0('Top 20 genes for Module ', top_modules[3],
' (MTcor = ', round(dataset$MTcor[dataset$Module == top_modules[3]][1],2),')')) %>%
kable_styling(full_width = F)
| Gene Symbol | MM | GS | SFARI Score | Relevance |
|---|---|---|---|---|
| MYCBP2 | 0.8196944 | 0.2898447 | Others | 0.5547696 |
| DENND5B | 0.6693310 | 0.3577416 | Others | 0.5135363 |
| RIMS2 | 0.7596851 | 0.2572706 | Others | 0.5084779 |
| ARHGAP32 | 0.7650873 | 0.2334209 | 3 | 0.4992541 |
| MEF2C | 0.8236810 | 0.1712714 | 3 | 0.4974762 |
| MYO9A | 0.4798712 | 0.5049781 | Others | 0.4924246 |
| FZD3 | 0.6064150 | 0.3588440 | Others | 0.4826295 |
| CDKL5 | 0.6059903 | 0.2699966 | 1 | 0.4379934 |
| BICD1 | 0.6759105 | 0.1920989 | Others | 0.4340047 |
| NRXN1 | 0.7912961 | 0.0742731 | 1 | 0.4327846 |
| TTBK2 | 0.6799173 | 0.1518390 | Others | 0.4158782 |
| GABRB2 | 0.7448393 | 0.0771744 | Others | 0.4110069 |
| SCN2A | 0.7331332 | 0.0740752 | 1 | 0.4036042 |
| NRXN3 | 0.5854827 | 0.2183796 | 1 | 0.4019312 |
| UBR5 | 0.5687603 | 0.2312289 | 2 | 0.3999946 |
| RYR2 | 0.6766000 | 0.1172778 | Others | 0.3969389 |
| SYT1 | 0.6696169 | -0.1157074 | Others | 0.3926622 |
| TSHZ3 | 0.5527810 | 0.2308938 | 1 | 0.3918374 |
| GABRA1 | 0.7207382 | 0.0176543 | Others | 0.3691963 |
| PLCB1 | 0.6967621 | -0.0130355 | 2 | 0.3548988 |
rm(create_table, i)
The top genes tend to have a relatively high mean level of expression
pca = datExpr %>% prcomp
ids = c()
for(tg in top_genes) ids = c(ids, tg$ID)
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% dplyr::select(ID, PC1, PC2, Module, gene.score) %>%
mutate(color = ifelse(Module %in% top_modules, as.character(Module), 'gray')) %>%
mutate(alpha = ifelse(color %in% top_modules & ID %in% ids, 1, 0.1))
plot_data %>% ggplot(aes(PC1, PC2)) + geom_point(alpha=plot_data$alpha, color=plot_data$color) +
xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1],2),'%)')) +
ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2],2),'%)')) +
theme_minimal() + ggtitle('Most relevant genes for top Modules')
rm(ids, pca, tg, plot_data)
Using the package clusterProfiler. Performing Gene Set Enrichment Analysis (GSEA) and Over Representation Analysis (ORA) using the following datasets:
Gene Ontology
Disease Ontology
Disease Gene Network
KEGG
REACTOME
# GSEA
load('./../Data/GSEA.RData')
# Rename lists
GSEA_GO = enrichment_GO
GSEA_DGN = enrichment_DGN
GSEA_DO = enrichment_DO
GSEA_KEGG = enrichment_KEGG
GSEA_Reactome = enrichment_Reactome
GSEA_SFARI = enrichment_SFARI
# ORA
load('./../Data/ORA.RData')
# Rename lists
ORA_GO = enrichment_GO
ORA_DGN = enrichment_DGN
ORA_DO = enrichment_DO
ORA_KEGG = enrichment_KEGG
ORA_Reactome = enrichment_Reactome
ORA_SFARI = enrichment_SFARI
rm(enrichment_GO, enrichment_DO, enrichment_DGN, enrichment_KEGG, enrichment_Reactome)
compare_methods = function(GSEA_list, ORA_list){
for(top_module in top_modules){
cat(paste0(' \n \nEnrichments for Module ', top_module, ' (MTcor=',
round(dataset$MTcor[dataset$Module==top_module][1],2), '): \n \n'))
GSEA = GSEA_list[[top_module]]
ORA = ORA_list[[top_module]]
cat(paste0('GSEA has ', nrow(GSEA), ' enriched terms \n'))
cat(paste0('ORA has ', nrow(ORA), ' enriched terms \n'))
cat(paste0(sum(ORA$ID %in% GSEA$ID), ' terms are enriched in both methods \n \n'))
plot_data = GSEA %>% mutate(pval_GSEA = p.adjust) %>% dplyr::select(ID, Description, NES, pval_GSEA) %>%
inner_join(ORA %>% mutate(pval_ORA = p.adjust) %>%
dplyr::select(ID, pval_ORA, GeneRatio, qvalue), by = 'ID')
if(nrow(plot_data)>0){
print(plot_data %>% mutate(pval_mean = pval_ORA + pval_GSEA) %>%
arrange(pval_mean) %>% dplyr::select(-pval_mean) %>%
kable %>% kable_styling(full_width = F))
}
}
}
plot_results = function(GSEA_list, ORA_list){
l = htmltools::tagList()
for(i in 1:length(top_modules)){
GSEA = GSEA_list[[top_modules[i]]]
ORA = ORA_list[[top_modules[i]]]
plot_data = GSEA %>% mutate(pval_GSEA = p.adjust) %>% dplyr::select(ID, Description, NES, pval_GSEA) %>%
inner_join(ORA %>% mutate(pval_ORA = p.adjust) %>% dplyr::select(ID, pval_ORA), by = 'ID')
if(nrow(plot_data)>5){
min_val = min(min(plot_data$pval_GSEA), min(plot_data$pval_ORA))
max_val = max(max(max(plot_data$pval_GSEA), max(plot_data$pval_ORA)),0.05)
ggp = ggplotly(plot_data %>% ggplot(aes(pval_GSEA, pval_ORA, color = NES)) +
geom_point(aes(id = Description)) +
geom_vline(xintercept = 0.05, color = 'gray', linetype = 'dotted') +
geom_hline(yintercept = 0.05, color = 'gray', linetype = 'dotted') +
ggtitle(paste0('Enriched terms in common for Module ', top_modules[i])) +
scale_x_continuous(limits = c(min_val, max_val)) +
scale_y_continuous(limits = c(min_val, max_val)) +
xlab('Corrected p-value for GSEA') + ylab('Corrected p-value for ORA') +
scale_colour_viridis(direction = -1) + theme_minimal() + coord_fixed())
l[[i]] = ggp
}
}
return(l)
}
compare_methods(GSEA_KEGG, ORA_KEGG)
Enrichments for Module #00BBDA (MTcor=-0.42):
GSEA has 8 enriched terms
ORA has 0 enriched terms
0 terms are enriched in both methods
Enrichments for Module #00B933 (MTcor=0.35):
GSEA has 2 enriched terms
ORA has 5 enriched terms
2 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| hsa03008 | Ribosome biogenesis in eukaryotes | 2.067246 | 0.0185047 | 0.0128670 | 9/200 | 0.0035037 |
| hsa05322 | Systemic lupus erythematosus | 2.014849 | 0.0394820 | 0.0000999 | 14/200 | 0.0000999 |
Enrichments for Module #FD61D0 (MTcor=0.19):
GSEA has 10 enriched terms
ORA has 2 enriched terms
0 terms are enriched in both methods
compare_methods(GSEA_Reactome, ORA_Reactome)
Enrichments for Module #00BBDA (MTcor=-0.42):
GSEA has 28 enriched terms
ORA has 0 enriched terms
0 terms are enriched in both methods
Enrichments for Module #00B933 (MTcor=0.35):
GSEA has 54 enriched terms
ORA has 81 enriched terms
50 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| R-HSA-1640170 | Cell Cycle | 1.721185 | 0.0113650 | 0.0000001 | 52/321 | 0.0000000 |
| R-HSA-3247509 | Chromatin modifying enzymes | 1.947809 | 0.0127978 | 0.0000000 | 33/321 | 0.0000000 |
| R-HSA-4839726 | Chromatin organization | 1.947809 | 0.0127978 | 0.0000000 | 33/321 | 0.0000000 |
| R-HSA-3108232 | SUMO E3 ligases SUMOylate target proteins | 1.922789 | 0.0133653 | 0.0000000 | 29/321 | 0.0000000 |
| R-HSA-212165 | Epigenetic regulation of gene expression | 2.031024 | 0.0141667 | 0.0000000 | 23/321 | 0.0000000 |
| R-HSA-211000 | Gene Silencing by RNA | 2.160193 | 0.0145551 | 0.0000011 | 19/321 | 0.0000000 |
| R-HSA-2559580 | Oxidative Stress Induced Senescence | 2.143811 | 0.0147222 | 0.0000000 | 20/321 | 0.0000000 |
| R-HSA-5250913 | Positive epigenetic regulation of rRNA expression | 2.225877 | 0.0148508 | 0.0000000 | 20/321 | 0.0000000 |
| R-HSA-5578749 | Transcriptional regulation by small RNAs | 2.130850 | 0.0149524 | 0.0000010 | 17/321 | 0.0000000 |
| R-HSA-3214815 | HDACs deacetylate histones | 2.192155 | 0.0151443 | 0.0000000 | 19/321 | 0.0000000 |
| R-HSA-8936459 | RUNX1 regulates genes involved in megakaryocyte differentiation and platelet function | 2.266836 | 0.0151443 | 0.0000002 | 17/321 | 0.0000000 |
| R-HSA-3214858 | RMTs methylate histone arginines | 2.156240 | 0.0151912 | 0.0000010 | 16/321 | 0.0000000 |
| R-HSA-3214841 | PKMTs methylate histone lysines | 2.265960 | 0.0152410 | 0.0000000 | 20/321 | 0.0000000 |
| R-HSA-5250924 | B-WICH complex positively regulates rRNA expression | 2.229208 | 0.0152410 | 0.0000001 | 17/321 | 0.0000000 |
| R-HSA-1221632 | Meiotic synapsis | 2.103650 | 0.0157116 | 0.0000000 | 17/321 | 0.0000000 |
| R-HSA-427389 | ERCC6 (CSB) and EHMT2 (G9a) positively regulate rRNA expression | 2.138078 | 0.0157659 | 0.0000000 | 17/321 | 0.0000000 |
| R-HSA-5693571 | Nonhomologous End-Joining (NHEJ) | 2.312576 | 0.0157659 | 0.0000000 | 16/321 | 0.0000000 |
| R-HSA-2299718 | Condensation of Prophase Chromosomes | 2.371031 | 0.0158198 | 0.0000000 | 16/321 | 0.0000000 |
| R-HSA-606279 | Deposition of new CENPA-containing nucleosomes at the centromere | 2.415026 | 0.0158902 | 0.0000000 | 16/321 | 0.0000000 |
| R-HSA-774815 | Nucleosome assembly | 2.415026 | 0.0158902 | 0.0000000 | 16/321 | 0.0000000 |
| R-HSA-912446 | Meiotic recombination | 2.163160 | 0.0158902 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-212300 | PRC2 methylates histones and DNA | 2.163053 | 0.0159782 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-110328 | Recognition and association of DNA glycosylase with site containing an affected pyrimidine | 2.175060 | 0.0162342 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-110329 | Cleavage of the damaged pyrimidine | 2.175060 | 0.0162342 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-73928 | Depyrimidination | 2.175060 | 0.0162342 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-73929 | Base-Excision Repair, AP Site Formation | 2.175060 | 0.0162342 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-5625886 | Activated PKN1 stimulates transcription of AR (androgen receptor) regulated genes KLK2 and KLK3 | 2.255317 | 0.0162342 | 0.0000000 | 14/321 | 0.0000000 |
| R-HSA-427359 | SIRT1 negatively regulates rRNA expression | 2.197242 | 0.0163913 | 0.0000000 | 14/321 | 0.0000000 |
| R-HSA-5334118 | DNA methylation | 2.217968 | 0.0163913 | 0.0000000 | 14/321 | 0.0000000 |
| R-HSA-73728 | RNA Polymerase I Promoter Opening | 2.269001 | 0.0163913 | 0.0000000 | 14/321 | 0.0000000 |
| R-HSA-110330 | Recognition and association of DNA glycosylase with site containing an affected purine | 2.373844 | 0.0164526 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-110331 | Cleavage of the damaged purine | 2.373844 | 0.0164526 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-171306 | Packaging Of Telomere Ends | 2.373844 | 0.0164526 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-73927 | Depurination | 2.373844 | 0.0164526 | 0.0000000 | 15/321 | 0.0000000 |
| R-HSA-2990846 | SUMOylation | 1.873891 | 0.0265519 | 0.0000000 | 29/321 | 0.0000000 |
| R-HSA-5620912 | Anchoring of the basal body to the plasma membrane | 2.162389 | 0.0145120 | 0.0122982 | 14/321 | 0.0001410 |
| R-HSA-5625740 | RHO GTPases activate PKNs | 2.123409 | 0.0302885 | 0.0000016 | 16/321 | 0.0000000 |
| R-HSA-5693565 | Recruitment and ATM-mediated phosphorylation of repair and signaling proteins at DNA double strand breaks | 2.120408 | 0.0311157 | 0.0000000 | 18/321 | 0.0000000 |
| R-HSA-5693606 | DNA Double Strand Break Response | 2.120408 | 0.0311157 | 0.0000000 | 18/321 | 0.0000000 |
| R-HSA-68886 | M Phase | 1.752112 | 0.0363589 | 0.0000162 | 36/321 | 0.0000002 |
| R-HSA-5617833 | Cilium Assembly | 1.910764 | 0.0397134 | 0.0004538 | 23/321 | 0.0000055 |
| R-HSA-8953854 | Metabolism of RNA | 1.723945 | 0.0109490 | 0.0456673 | 48/321 | 0.0004968 |
| R-HSA-72203 | Processing of Capped Intron-Containing Pre-mRNA | 1.783812 | 0.0503212 | 0.0069476 | 26/321 | 0.0000808 |
| R-HSA-1500620 | Meiosis | 2.062276 | 0.0607647 | 0.0000000 | 18/321 | 0.0000000 |
| R-HSA-1912408 | Pre-NOTCH Transcription and Translation | 2.039518 | 0.0607647 | 0.0000010 | 16/321 | 0.0000000 |
| R-HSA-3214842 | HDMs demethylate histones | 2.084814 | 0.0642100 | 0.0000000 | 16/321 | 0.0000000 |
| R-HSA-9018519 | Estrogen-dependent gene expression | 1.882479 | 0.0697179 | 0.0019346 | 18/321 | 0.0000228 |
| R-HSA-1474165 | Reproduction | 2.009032 | 0.0903052 | 0.0000000 | 18/321 | 0.0000000 |
| R-HSA-69278 | Cell Cycle, Mitotic | 1.636632 | 0.0929054 | 0.0000637 | 42/321 | 0.0000008 |
| R-HSA-1852241 | Organelle biogenesis and maintenance | 1.723768 | 0.0493833 | 0.0610793 | 26/321 | 0.0006560 |
Enrichments for Module #FD61D0 (MTcor=0.19):
GSEA has 38 enriched terms
ORA has 2 enriched terms
1 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| R-HSA-112316 | Neuronal System | 1.817456 | 0.0108269 | 0.0779653 | 8/36 | 0.0365206 |
Plots of the results when there are more than 5 terms in common between methods:
plot_results(GSEA_Reactome, ORA_Reactome)
compare_methods(GSEA_GO, ORA_GO)
Enrichments for Module #00BBDA (MTcor=-0.42):
GSEA has 26 enriched terms
ORA has 3 enriched terms
0 terms are enriched in both methods
Enrichments for Module #00B933 (MTcor=0.35):
GSEA has 71 enriched terms
ORA has 107 enriched terms
53 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| GO:0051276 | chromosome organization | 1.910729 | 0.0475851 | 0.0000000 | 86/488 | 0.0000000 |
| GO:0006396 | RNA processing | 2.083744 | 0.0477177 | 0.0001086 | 68/488 | 0.0000025 |
| GO:0006259 | DNA metabolic process | 1.688344 | 0.0484306 | 0.0000104 | 65/488 | 0.0000004 |
| GO:0006974 | cellular response to DNA damage stimulus | 1.626043 | 0.0488029 | 0.0001675 | 59/488 | 0.0000035 |
| GO:0006325 | chromatin organization | 2.092795 | 0.0497765 | 0.0000000 | 68/488 | 0.0000000 |
| GO:0007017 | microtubule-based process | 1.850879 | 0.0504519 | 0.0000341 | 51/488 | 0.0000010 |
| GO:0070925 | organelle assembly | 1.753699 | 0.0498979 | 0.0013949 | 50/488 | 0.0000261 |
| GO:0006397 | mRNA processing | 2.092783 | 0.0513932 | 0.0002556 | 45/488 | 0.0000051 |
| GO:0008380 | RNA splicing | 2.053987 | 0.0523666 | 0.0005267 | 41/488 | 0.0000102 |
| GO:0044782 | cilium organization | 1.962341 | 0.0555675 | 0.0001139 | 33/488 | 0.0000025 |
| GO:0060271 | cilium assembly | 1.998239 | 0.0558887 | 0.0000434 | 33/488 | 0.0000012 |
| GO:0000226 | microtubule cytoskeleton organization | 1.869246 | 0.0533070 | 0.0042517 | 36/488 | 0.0000724 |
| GO:0071103 | DNA conformation change | 1.971281 | 0.0586526 | 0.0000000 | 32/488 | 0.0000000 |
| GO:0031503 | protein-containing complex localization | 1.935758 | 0.0578412 | 0.0020103 | 26/488 | 0.0000361 |
| GO:0071824 | protein-DNA complex subunit organization | 2.027556 | 0.0598072 | 0.0000766 | 25/488 | 0.0000019 |
| GO:0006338 | chromatin remodeling | 2.382088 | 0.0606265 | 0.0000000 | 29/488 | 0.0000000 |
| GO:0006333 | chromatin assembly or disassembly | 2.198934 | 0.0615305 | 0.0000009 | 25/488 | 0.0000000 |
| GO:0006323 | DNA packaging | 2.073797 | 0.0618323 | 0.0000004 | 25/488 | 0.0000000 |
| GO:0034728 | nucleosome organization | 2.216471 | 0.0627335 | 0.0000017 | 23/488 | 0.0000001 |
| GO:0031497 | chromatin assembly | 2.085017 | 0.0636927 | 0.0000008 | 22/488 | 0.0000000 |
| GO:0060147 | regulation of posttranscriptional gene silencing | 2.027969 | 0.0648657 | 0.0000940 | 18/488 | 0.0000022 |
| GO:0060964 | regulation of gene silencing by miRNA | 2.027969 | 0.0648657 | 0.0000940 | 18/488 | 0.0000022 |
| GO:0060966 | regulation of gene silencing by RNA | 2.027969 | 0.0648657 | 0.0000940 | 18/488 | 0.0000022 |
| GO:0006334 | nucleosome assembly | 2.080711 | 0.0650384 | 0.0000013 | 20/488 | 0.0000000 |
| GO:0045638 | negative regulation of myeloid cell differentiation | 2.150275 | 0.0653078 | 0.0002379 | 17/488 | 0.0000048 |
| GO:0030219 | megakaryocyte differentiation | 2.116928 | 0.0657991 | 0.0000120 | 18/488 | 0.0000004 |
| GO:0043044 | ATP-dependent chromatin remodeling | 2.399869 | 0.0667354 | 0.0000000 | 22/488 | 0.0000000 |
| GO:0000726 | non-recombinational repair | 2.103023 | 0.0668988 | 0.0000762 | 16/488 | 0.0000019 |
| GO:0045652 | regulation of megakaryocyte differentiation | 2.192708 | 0.0672609 | 0.0000004 | 18/488 | 0.0000000 |
| GO:0006303 | double-strand break repair via nonhomologous end joining | 2.133881 | 0.0675149 | 0.0000231 | 16/488 | 0.0000007 |
| GO:0006336 | DNA replication-independent nucleosome assembly | 2.285467 | 0.0701684 | 0.0000000 | 17/488 | 0.0000000 |
| GO:0034724 | DNA replication-independent nucleosome organization | 2.285467 | 0.0701684 | 0.0000000 | 17/488 | 0.0000000 |
| GO:0034508 | centromere complex assembly | 2.319020 | 0.0701684 | 0.0000001 | 16/488 | 0.0000000 |
| GO:0043486 | histone exchange | 2.424400 | 0.0709940 | 0.0000000 | 16/488 | 0.0000000 |
| GO:0031055 | chromatin remodeling at centromere | 2.323129 | 0.0712170 | 0.0000000 | 16/488 | 0.0000000 |
| GO:0016233 | telomere capping | 2.163511 | 0.0714263 | 0.0000000 | 16/488 | 0.0000000 |
| GO:0034080 | CENP-A containing nucleosome assembly | 2.437971 | 0.0723020 | 0.0000000 | 16/488 | 0.0000000 |
| GO:0061641 | CENP-A containing chromatin organization | 2.437971 | 0.0723020 | 0.0000000 | 16/488 | 0.0000000 |
| GO:0000183 | chromatin silencing at rDNA | 2.227051 | 0.0733032 | 0.0000000 | 15/488 | 0.0000000 |
| GO:0006335 | DNA replication-dependent nucleosome assembly | 2.284645 | 0.0745075 | 0.0000000 | 14/488 | 0.0000000 |
| GO:0034723 | DNA replication-dependent nucleosome organization | 2.284645 | 0.0745075 | 0.0000000 | 14/488 | 0.0000000 |
| GO:0034968 | histone lysine methylation | 2.230454 | 0.0652216 | 0.0110059 | 15/488 | 0.0001748 |
| GO:0120031 | plasma membrane bounded cell projection assembly | 1.759826 | 0.0518964 | 0.0370942 | 38/488 | 0.0005175 |
| GO:0000375 | RNA splicing, via transesterification reactions | 1.971013 | 0.0542972 | 0.0374651 | 31/488 | 0.0005175 |
| GO:0030031 | cell projection assembly | 1.747850 | 0.0517846 | 0.0487325 | 38/488 | 0.0006362 |
| GO:0018023 | peptidyl-lysine trimethylation | 2.209662 | 0.0725920 | 0.0320544 | 9/488 | 0.0004559 |
| GO:0018022 | peptidyl-lysine methylation | 2.056097 | 0.0641787 | 0.0453486 | 15/488 | 0.0006003 |
| GO:0006406 | mRNA export from nucleus | 2.125568 | 0.0653078 | 0.0444143 | 14/488 | 0.0005962 |
| GO:0071427 | mRNA-containing ribonucleoprotein complex export from nucleus | 2.125568 | 0.0653078 | 0.0444143 | 14/488 | 0.0005962 |
| GO:0006405 | RNA export from nucleus | 2.140491 | 0.0641198 | 0.0562618 | 15/488 | 0.0007149 |
| GO:0000377 | RNA splicing, via transesterification reactions with bulged adenosine as nucleophile | 1.951349 | 0.0544377 | 0.0729604 | 30/488 | 0.0008914 |
| GO:0000398 | mRNA splicing, via spliceosome | 1.951349 | 0.0544377 | 0.0729604 | 30/488 | 0.0008914 |
| GO:0097711 | ciliary basal body-plasma membrane docking | 2.158110 | 0.0650384 | 0.0704318 | 14/488 | 0.0008832 |
Enrichments for Module #FD61D0 (MTcor=0.19):
GSEA has 42 enriched terms
ORA has 23 enriched terms
1 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| GO:0050877 | nervous system process | 1.6683 | 0.0460065 | 0.0002512 | 15/52 | 0.000215 |
Plots of the results when there are more than 5 terms in common between methods:
plot_results(GSEA_GO, ORA_GO)
compare_methods(GSEA_DO, ORA_DO)
Enrichments for Module #00BBDA (MTcor=-0.42):
GSEA has 0 enriched terms
ORA has 0 enriched terms
0 terms are enriched in both methods
Enrichments for Module #00B933 (MTcor=0.35):
GSEA has 1 enriched terms
ORA has 0 enriched terms
0 terms are enriched in both methods
Enrichments for Module #FD61D0 (MTcor=0.19):
GSEA has 7 enriched terms
ORA has 5 enriched terms
3 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| DOID:0060040 | pervasive developmental disorder | 2.190339 | 0.0072451 | 0.0112162 | 7/33 | 0.0036588 |
| DOID:0060041 | autism spectrum disorder | 2.154444 | 0.0073129 | 0.0706203 | 6/33 | 0.0138221 |
| DOID:12849 | autistic disorder | 2.154444 | 0.0073129 | 0.0706203 | 6/33 | 0.0138221 |
compare_methods(GSEA_DGN, ORA_DGN)
Enrichments for Module #00BBDA (MTcor=-0.42):
GSEA has 0 enriched terms
ORA has 1 enriched terms
0 terms are enriched in both methods
Enrichments for Module #00B933 (MTcor=0.35):
GSEA has 0 enriched terms
ORA has 6 enriched terms
0 terms are enriched in both methods
Enrichments for Module #FD61D0 (MTcor=0.19):
GSEA has 15 enriched terms
ORA has 13 enriched terms
3 terms are enriched in both methods
| ID | Description | NES | pval_GSEA | pval_ORA | GeneRatio | qvalue |
|---|---|---|---|---|---|---|
| umls:C0014544 | Epilepsy | 1.780997 | 0.0234847 | 0.0006046 | 13/50 | 0.0001374 |
| umls:C0004352 | Autistic Disorder | 1.875717 | 0.0234003 | 0.0063712 | 12/50 | 0.0011580 |
| umls:C1096063 | Drug Resistant Epilepsy | 2.174315 | 0.0324183 | 0.0000664 | 6/50 | 0.0000603 |
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] WGCNA_1.69 fastcluster_1.1.25 dynamicTreeCut_1.63-1
## [4] org.Hs.eg.db_3.8.2 AnnotationDbi_1.46.1 IRanges_2.18.3
## [7] S4Vectors_0.22.1 Biobase_2.44.0 BiocGenerics_0.30.0
## [10] DOSE_3.10.2 ReactomePA_1.28.0 clusterProfiler_3.12.0
## [13] biomaRt_2.40.5 kableExtra_1.1.0 knitr_1.28
## [16] doParallel_1.0.15 iterators_1.0.12 foreach_1.5.0
## [19] polycor_0.7-10 expss_0.10.2 ggpubr_0.2.5
## [22] magrittr_1.5 GGally_1.5.0 gridExtra_2.3
## [25] viridis_0.5.1 viridisLite_0.3.0 RColorBrewer_1.1-2
## [28] dendextend_1.13.4 plotly_4.9.2 glue_1.4.1
## [31] reshape2_1.4.4 forcats_0.5.0 stringr_1.4.0
## [34] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [37] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [40] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.0 RSQLite_2.2.0
## [3] htmlwidgets_1.5.1 grid_3.6.3
## [5] BiocParallel_1.18.1 munsell_0.5.0
## [7] codetools_0.2-16 preprocessCore_1.46.0
## [9] withr_2.2.0 colorspace_1.4-1
## [11] GOSemSim_2.10.0 highr_0.8
## [13] rstudioapi_0.11 ggsignif_0.6.0
## [15] labeling_0.3 urltools_1.7.3
## [17] GenomeInfoDbData_1.2.1 polyclip_1.10-0
## [19] bit64_0.9-7 farver_2.0.3
## [21] vctrs_0.3.1 generics_0.0.2
## [23] xfun_0.12 R6_2.4.1
## [25] GenomeInfoDb_1.20.0 graphlayouts_0.7.0
## [27] locfit_1.5-9.4 DelayedArray_0.10.0
## [29] bitops_1.0-6 reshape_0.8.8
## [31] fgsea_1.10.1 gridGraphics_0.5-0
## [33] assertthat_0.2.1 scales_1.1.1
## [35] ggraph_2.0.3 nnet_7.3-14
## [37] enrichplot_1.4.0 gtable_0.3.0
## [39] tidygraph_1.2.0 rlang_0.4.6
## [41] genefilter_1.66.0 splines_3.6.3
## [43] lazyeval_0.2.2 acepack_1.4.1
## [45] impute_1.58.0 broom_0.5.5
## [47] europepmc_0.4 checkmate_2.0.0
## [49] BiocManager_1.30.10 yaml_2.2.1
## [51] modelr_0.1.6 crosstalk_1.1.0.1
## [53] backports_1.1.8 qvalue_2.16.0
## [55] Hmisc_4.4-0 tools_3.6.3
## [57] ggplotify_0.0.5 ellipsis_0.3.1
## [59] ggridges_0.5.2 Rcpp_1.0.4.6
## [61] plyr_1.8.6 base64enc_0.1-3
## [63] progress_1.2.2 zlibbioc_1.30.0
## [65] RCurl_1.98-1.2 prettyunits_1.1.1
## [67] rpart_4.1-15 cowplot_1.0.0
## [69] SummarizedExperiment_1.14.1 haven_2.2.0
## [71] ggrepel_0.8.2 cluster_2.1.0
## [73] fs_1.4.0 data.table_1.12.8
## [75] DO.db_2.9 triebeard_0.3.0
## [77] reprex_0.3.0 reactome.db_1.68.0
## [79] matrixStats_0.56.0 xtable_1.8-4
## [81] hms_0.5.3 evaluate_0.14
## [83] XML_3.99-0.3 jpeg_0.1-8.1
## [85] readxl_1.3.1 compiler_3.6.3
## [87] crayon_1.3.4 htmltools_0.4.0
## [89] mgcv_1.8-31 Formula_1.2-3
## [91] geneplotter_1.62.0 lubridate_1.7.4
## [93] DBI_1.1.0 tweenr_1.0.1
## [95] dbplyr_1.4.2 MASS_7.3-51.6
## [97] rappdirs_0.3.1 Matrix_1.2-18
## [99] cli_2.0.2 igraph_1.2.5
## [101] GenomicRanges_1.36.1 pkgconfig_2.0.3
## [103] rvcheck_0.1.8 foreign_0.8-76
## [105] xml2_1.2.5 annotate_1.62.0
## [107] webshot_0.5.2 XVector_0.24.0
## [109] rvest_0.3.5 digest_0.6.25
## [111] graph_1.62.0 rmarkdown_2.1
## [113] cellranger_1.1.0 fastmatch_1.1-0
## [115] htmlTable_1.13.3 curl_4.3
## [117] graphite_1.30.0 lifecycle_0.2.0
## [119] nlme_3.1-147 jsonlite_1.7.0
## [121] fansi_0.4.1 pillar_1.4.4
## [123] lattice_0.20-41 httr_1.4.1
## [125] survival_3.1-12 GO.db_3.8.2
## [127] UpSetR_1.4.0 png_0.1-7
## [129] bit_1.1-15.2 ggforce_0.3.1
## [131] stringi_1.4.6 blob_1.2.1
## [133] DESeq2_1.24.0 latticeExtra_0.6-29
## [135] memoise_1.1.0